---
title: Supabase Hybrid Search
---

LangChain supports hybrid search with a Supabase Postgres database. The hybrid search combines the postgres `pgvector` extension (similarity search) and Full-Text Search (keyword search) to retrieve documents. You can add documents via SupabaseVectorStore `addDocuments` function. SupabaseHybridKeyWordSearch accepts embedding, supabase client, number of results for similarity search, and number of results for keyword search as parameters. The `getRelevantDocuments` function produces a list of documents that has duplicates removed and is sorted by relevance score.

## Setup

### Install the library with

```bash npm
npm install -S @supabase/supabase-js
```
### Create a table and search functions in your database

Run this in your database:

```sql
-- Enable the pgvector extension to work with embedding vectors
create extension vector;

-- Create a table to store your documents
create table documents (
  id bigserial primary key,
  content text, -- corresponds to Document.pageContent
  metadata jsonb, -- corresponds to Document.metadata
  embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
);

-- Create a function to similarity search for documents
create function match_documents (
  query_embedding vector(1536),
  match_count int DEFAULT null,
  filter jsonb DEFAULT '{}'
) returns table (
  id bigint,
  content text,
  metadata jsonb,
  similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where metadata @> filter
  order by documents.embedding <=> query_embedding
  limit match_count;
end;
$$;

-- Create a function to keyword search for documents
create function kw_match_documents(query_text text, match_count int)
returns table (id bigint, content text, metadata jsonb, similarity real)
as $$

begin
return query execute
format('select id, content, metadata, ts_rank(to_tsvector(content), plainto_tsquery($1)) as similarity
from documents
where to_tsvector(content) @@ plainto_tsquery($1)
order by similarity desc
limit $2')
using query_text, match_count;
end;
$$ language plpgsql;
```
## Usage

import IntegrationInstallTooltip from '/snippets/javascript-integrations/integration-install-tooltip.mdx';

<IntegrationInstallTooltip/>

```bash npm
npm install @langchain/openai @langchain/community @langchain/core
```

import SupabaseHybrid from "/snippets/javascript-integrations/examples/retrievers/supabase_hybrid.mdx";

<SupabaseHybrid />

## Related

- [Retrieval guide](/oss/langchain/retrieval)
